Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system, comprising: a processor; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: receive a set of observed symptoms of a physical system; match the set of observed symptoms with a database of fault scenarios to generate a plurality of matched fault scenarios, wherein the database of fault scenarios comprises combinations of symptoms and corresponding root cause identifiers; and refine the plurality of matched fault scenarios, comprising to: determine whether a first fault scenario of the plurality of matched fault scenarios is to be subsumed by a second fault scenario; and in response to a determination that the first fault scenario of the plurality of matched fault scenarios is to be subsumed by the second fault scenario: include the second fault scenario in the plurality of matched fault scenarios, deprioritize the first fault scenario, or both include the second fault scenario in the plurality of matched fault scenarios and deprioritize the first fault scenario; and output the plurality of matched fault scenarios as potential root causes.
2. The system of claim 1 , wherein: the second fault scenario is one of the plurality of matched fault scenarios; and to determine whether the first fault scenario of the plurality of matched fault scenario is to be subsumed by the second fault scenario includes to pairwise compare the plurality of matched fault scenarios.
3. The system of claim 1 , wherein to deprioritize the first fault scenario comprises to remove the first fault scenario from the plurality of matched fault scenarios.
4. The system of claim 1 , wherein to deprioritize the first fault scenario comprises to recognize the first fault scenario as an alternative explanation associated with one or more root causes that led to the set of observed symptoms.
5. The system of claim 1 , wherein the database of fault scenarios further comprises one or more confidence levels.
6. The system of claim 5 , wherein to refine the plurality of matched fault scenarios further includes associating matched fault scenarios according to relationships.
The system is designed for fault diagnosis in technical systems, particularly for identifying and refining potential fault scenarios based on observed symptoms. The core problem addressed is the ambiguity in fault diagnosis, where multiple potential fault scenarios may match the observed symptoms, making it difficult to pinpoint the exact cause. The system improves upon this by refining the matched fault scenarios through additional analysis, including associating them based on relationships between different fault scenarios. This helps narrow down the most likely causes by considering how different faults may interact or be related. The system may also prioritize fault scenarios based on factors such as likelihood, severity, or historical data, further enhancing diagnostic accuracy. The relationships between fault scenarios could include causal links, common underlying causes, or dependencies between different faults. By refining and associating fault scenarios in this way, the system provides a more precise and actionable diagnosis, reducing the time and effort required for troubleshooting. The system may be applied in various technical domains, including industrial machinery, automotive systems, or electronic devices, where accurate fault diagnosis is critical for maintenance and repair operations.
7. The system of claim 6 , wherein the relationships include at least one of the following: symptom relationships, root cause identifier relationships, and confidence level relationships.
8. The system of claim 1 wherein to refine the plurality of matched fault scenarios further comprises to order the plurality of matched fault scenarios according to confidence levels associated with a given matched fault scenario.
9. The system of claim 1 , wherein the first fault scenario is determined to be subsumed by the second fault scenario in response to a determination that the first fault scenario is a base scenario and the second fault scenario is a derived scenario from the base scenario.
10. The system of claim 9 , wherein it is determined that the first fault scenario is the base scenario and the second fault scenario is the derived scenario from the base scenario, the determination being made in response to the first fault scenario and second matched fault scenario sharing a same root cause identifier and a confidence level of the first fault scenario being lower than a confidence level of the second matched fault scenario.
This invention relates to fault detection and analysis systems, specifically for determining relationships between different fault scenarios in a monitored system. The problem addressed is the challenge of identifying and distinguishing between base fault scenarios (primary root causes) and derived fault scenarios (secondary or related faults) to improve diagnostic accuracy and efficiency. The system analyzes fault scenarios detected in a monitored system, comparing them to identify relationships based on shared root causes and confidence levels. When two fault scenarios share the same root cause identifier, the system determines that one is a base scenario and the other is a derived scenario based on their confidence levels. The scenario with the lower confidence level is classified as the base scenario, while the higher-confidence scenario is classified as the derived scenario. This determination helps prioritize and streamline fault resolution by distinguishing between primary and secondary issues. The system may also include components for detecting faults, generating fault scenarios, and storing fault data, including identifiers and confidence levels. The comparison process involves matching fault scenarios based on their root cause identifiers and evaluating their confidence levels to establish the base-derived relationship. This approach enhances fault diagnosis by reducing redundancy and improving the accuracy of root cause identification.
11. The system of claim 1 , wherein in response to a determination that the first fault scenario is an alternative explanation for the second fault scenario, to refine the plurality of matched fault scenarios further comprises to indicate the first fault scenario and the second fault scenario as part of a root cause group.
12. The system of claim 11 , wherein the first fault scenario is determined to be the alternative explanation for the second fault scenario in response to the first fault scenario and second fault scenario sharing a same set of symptoms.
The system is designed for fault diagnosis in technical systems, particularly where multiple fault scenarios may present similar symptoms, leading to diagnostic ambiguity. The system analyzes fault scenarios to determine whether one scenario can serve as an alternative explanation for another when they share identical symptom sets. This helps distinguish between independent faults and scenarios where one fault may be misinterpreted as another. The system includes a fault detection module that identifies symptoms in the system, a fault scenario database containing predefined fault scenarios and their associated symptoms, and a diagnostic engine that compares symptom sets between scenarios. When two scenarios share the same symptoms, the system flags one as an alternative explanation for the other, improving diagnostic accuracy by reducing false positives and ensuring that the most likely root cause is identified. This is particularly useful in complex systems where multiple faults may interact or where sensor data is limited. The system may also prioritize scenarios based on additional factors such as historical data or system context to further refine diagnostics.
13. The system of claim 1 , wherein in response to a determination that the first fault scenario is a separate possible fault from the second fault scenario, to refine the plurality of matched fault scenarios further comprises to indicate the first fault scenario and second fault scenario are not part of a same root cause group.
14. The system of claim 13 , wherein in response to the first fault scenario and second fault scenario sharing neither a same set of symptoms nor a same root cause identifier, it is determined that the first fault scenario is the separate possible fault from the second fault scenario.
15. The system of claim 1 , wherein to determine whether the first fault scenario of the plurality of matched fault scenarios is to be subsumed by the second fault scenario comprises to: determine an expression that has the first fault scenario as a parameter and that corresponds to the second fault scenario; determine whether the expression satisfies a preset criterion; and in response to the expression satisfying the preset criterion, deem the first fault scenario as subsumed by the second fault scenario.
16. The system of claim 1 , wherein to match the set of observed symptoms includes to add a partial-match potential fault scenario (PMPFS) that handles partial matches with a matching mechanism.
17. The system of claim 16 , wherein to add the PMPFS comprises to allow a potential fault scenario to specify a given symptom as excluding one or more values.
18. The system of claim 16 , wherein to add the PMPFS comprises to allow a potential fault scenario to restrict a scope of the PMPFS and its symptoms and correspondingly reduce a probability associated with the PMPFS.
A system for managing potential fault scenarios in a technical or industrial environment is described. The system identifies and analyzes potential fault scenarios (PMPFS) that could occur within a system or process. These scenarios are evaluated to determine their scope, including the range of symptoms they may produce and the likelihood of their occurrence. The system then restricts the scope of each PMPFS by narrowing the possible symptoms and conditions under which the fault could manifest. By doing so, the system reduces the probability of the fault occurring or being detected, thereby improving system reliability and safety. The system may also prioritize or categorize these scenarios based on their severity, impact, or likelihood, allowing for more effective fault management and mitigation strategies. This approach helps in proactively addressing potential issues before they escalate into critical failures, ensuring smoother and more reliable system operation. The system may be applied in various domains, including industrial automation, aerospace, automotive, or any field where fault detection and prevention are critical.
19. The system of claim 16 , wherein to add the PMPFS comprises to allow a potential fault scenario to define an aggregate symptom across multiple inputs.
20. The system of claim 16 , wherein to add the PMPFS comprises to exclude from the PMPFS a set of back propagated symptoms.
A system for managing and analyzing medical data includes a patient monitoring and prediction framework (PMPFS) that processes physiological data from one or more patients to detect and predict adverse health events. The system collects real-time patient data, such as vital signs, lab results, and clinical observations, and applies machine learning algorithms to identify patterns indicative of potential complications. The PMPFS generates alerts or recommendations for healthcare providers based on the analysis, improving early intervention and patient outcomes. The system further includes a mechanism to refine the PMPFS by excluding a set of back-propagated symptoms. Back-propagated symptoms are those that may have been incorrectly associated with a predicted event due to noise, data errors, or misleading correlations. By removing these symptoms, the system enhances the accuracy and reliability of its predictions, reducing false positives and ensuring that only relevant and actionable insights are presented to clinicians. This exclusion process may involve statistical filtering, machine learning-based outlier detection, or domain-specific rules to identify and discard unreliable symptom data. The refined PMPFS then continues to monitor patients with improved predictive performance, supporting more effective clinical decision-making.
21. The system of claim 20 , wherein to add the PMPFS comprises at least one of the following: using a subset of back-propagated symptoms; using an aggregate of back-propagated symptoms; and using exclusion of back-propagated symptom values.
22. The system of claim 1 , wherein to match the set of observed symptoms includes to perform an approximate matching.
The system relates to a diagnostic tool for identifying issues in a technical system, such as machinery or software, by analyzing observed symptoms. The problem addressed is the difficulty in precisely matching observed symptoms to known issues due to variations in symptom presentation, incomplete data, or noise. The system improves upon prior art by using approximate matching techniques to identify potential matches even when exact matches are not possible. This involves comparing observed symptoms against a database of known symptom patterns, allowing for partial or probabilistic matches. The system may also incorporate additional data, such as historical records or contextual information, to refine the matching process. By using approximate matching, the system increases the likelihood of identifying relevant issues even when symptom data is incomplete or ambiguous. This approach enhances diagnostic accuracy and reduces the need for manual intervention in troubleshooting. The system may be applied in various domains, including industrial equipment monitoring, software debugging, or medical diagnostics, where precise symptom matching is challenging. The approximate matching technique may involve statistical methods, machine learning, or fuzzy logic to handle variations in symptom data. The overall goal is to provide a more robust and flexible diagnostic solution compared to exact matching systems.
23. A method, comprising: receiving a set of observed symptoms of a physical system; matching the set of observed symptoms with a database of fault scenarios to generate a plurality of matched fault scenarios, wherein the database of fault scenarios comprises combinations of symptoms and corresponding root cause identifiers; and refining the plurality of matched fault scenarios, comprising: determining whether a first fault scenario of the plurality of matched fault scenarios is to be subsumed by a second fault scenario; and in response to a determination that the first fault scenario of the plurality of matched fault scenarios is to be subsumed by the second fault scenario: including the second fault scenario in the plurality of matched fault scenarios, deprioritizing the first fault scenario, or both including the second fault scenario in the plurality of matched fault scenarios and deprioritizing the first fault scenario; and outputting the plurality of matched fault scenarios as potential root causes.
24. The method of claim 23 , wherein: the second fault scenario is one of the plurality of matched fault scenarios; and the determining of whether the first fault scenario of the plurality of matched fault scenario is to be subsumed by the second fault scenario includes pairwise comparing the plurality of matched fault scenarios.
25. The method of claim 23 , wherein the deprioritizing of the first fault scenario comprises removing the first fault scenario from the plurality of matched fault scenarios.
26. The method of claim 23 , wherein the deprioritizing of the first fault scenario comprises recognizing the first fault scenario as an alternative explanation associated with one or more root causes that led to the set of observed symptoms.
27. The method of claim 23 , wherein the database of fault scenarios also comprises one or more confidence levels.
A system and method for managing fault scenarios in industrial or automated systems involves a database storing predefined fault scenarios, each associated with one or more confidence levels. The confidence levels indicate the likelihood or certainty of a fault scenario occurring or being accurately detected. The database may also include additional data such as fault descriptions, diagnostic steps, or corrective actions. The system monitors operational parameters of the industrial system and compares them against the stored fault scenarios to identify potential faults. When a match is found, the system may trigger alerts, initiate automated responses, or provide guidance to operators based on the confidence level of the detected fault. The confidence levels help prioritize responses, ensuring critical faults are addressed promptly while lower-confidence scenarios may require further verification. This approach improves fault detection accuracy and reduces downtime by providing structured, confidence-weighted fault management. The system may integrate with existing monitoring and control systems to enhance reliability and efficiency in industrial operations.
28. The method of claim 27 , wherein the refining of the plurality of matched fault scenarios further includes associating matched fault scenarios according to relationships.
29. The method of claim 28 , wherein the relationships include at least one of the following: symptom relationships, root cause identifier relationships, and confidence level relationships.
30. The method of claim 23 , wherein the refining of the plurality of matched fault scenario further comprises ordering the plurality of matched fault scenarios according to confidence levels associated with a given matched fault scenario.
This invention relates to fault diagnosis in technical systems, specifically improving the accuracy and efficiency of identifying and ranking potential fault scenarios. The problem addressed is the challenge of determining the most likely causes of system malfunctions from a set of detected faults, where multiple scenarios may match the observed symptoms but with varying degrees of certainty. The method involves refining a set of matched fault scenarios by analyzing their associated confidence levels. These confidence levels are derived from factors such as the likelihood of each fault scenario occurring, the strength of evidence supporting it, and historical data. The matched fault scenarios are then ordered based on these confidence levels, prioritizing the most probable causes for further investigation or corrective action. This ranking helps operators or automated systems focus on the most critical issues first, reducing diagnostic time and improving system reliability. The method may also involve additional steps such as filtering out low-confidence scenarios or combining multiple sources of evidence to refine the confidence levels. By systematically evaluating and ranking fault scenarios, the invention enhances the precision of fault diagnosis, particularly in complex systems where multiple potential causes may exist. This approach is applicable to various domains, including industrial automation, automotive systems, and aerospace, where accurate and rapid fault identification is essential.
31. The method of claim 23 , wherein the first fault scenario is determined to be subsumed by the second fault scenario in response to a determination that the first fault scenario is a base scenario and the second fault scenario is a derived scenario from the base scenario.
32. The method of claim 31 , wherein it is determined that the first fault scenario is the base scenario and the second fault scenario is the derived scenario from the base scenario, the determination being made in response to the first fault scenario and second matched fault scenario sharing a same root cause identifier and a confidence level of the first fault scenario being lower than a confidence level of the second matched fault scenario.
33. The method of claim 23 , wherein in response to the first fault scenario being determined to be an alternative explanation for the second fault scenario, the refining of the plurality of matched fault scenario further comprises indicating the first fault scenario and second fault scenario as part of a root cause group.
This invention relates to fault diagnosis in technical systems, specifically improving the accuracy of identifying root causes by analyzing relationships between detected fault scenarios. The problem addressed is the difficulty in distinguishing between independent faults and those that are causally linked, leading to misdiagnosis or incomplete troubleshooting. The method involves comparing detected fault scenarios to a database of known fault scenarios to identify matches. When two fault scenarios are matched, the system evaluates whether one could be an alternative explanation for the other. If so, the system refines the analysis by grouping the two scenarios as part of a root cause group, indicating they share a common underlying cause. This grouping helps prioritize troubleshooting efforts and reduces redundant or conflicting diagnoses. The method may also involve additional steps such as calculating confidence scores for the matches, filtering out low-confidence matches, and adjusting the grouping based on system-specific rules or historical data. The goal is to improve diagnostic efficiency by focusing on the most likely root causes rather than treating each fault scenario in isolation. This approach is particularly useful in complex systems where multiple faults may appear simultaneously but stem from a single root cause.
34. The method of claim 33 , wherein the first fault scenario is determined to be the alternative explanation for the second fault scenario in response to the first fault scenario and second fault scenario sharing a same set of symptoms.
This invention relates to fault diagnosis in technical systems, specifically addressing the challenge of distinguishing between multiple potential fault scenarios that present identical symptoms. The method involves analyzing fault scenarios to determine whether one scenario can serve as an alternative explanation for another when both exhibit the same set of symptoms. By identifying such relationships, the system improves diagnostic accuracy by reducing ambiguity in fault identification. The method includes comparing fault scenarios based on their symptom profiles and determining if one scenario logically explains the observed symptoms in place of another. This approach helps prioritize fault resolution by eliminating redundant or less likely explanations, thereby enhancing system reliability and maintenance efficiency. The technique is particularly useful in complex systems where multiple faults can produce overlapping symptoms, such as in industrial machinery, automotive systems, or software applications. The method ensures that diagnostic processes are streamlined by focusing on the most plausible fault explanations, reducing downtime and maintenance costs.
35. The method of claim 23 , wherein in response to a determination that the first fault scenario is a separate possible fault from the second fault scenario, the refining of the plurality of matched fault scenario further comprises indicating the first fault scenario and second fault scenario are not part of a same root cause group.
36. The method of claim 35 , wherein in response to the first fault scenario and second fault scenario sharing neither a same set of symptoms nor a same root cause identifier, it is determined that the first fault scenario is the separate possible fault from the second fault scenario.
37. The method of claim 23 , wherein the refining of the plurality of matched fault scenarios further comprises: determining an expression that has the first fault scenario as a parameter and that corresponds to the second fault scenario; determining whether the expression satisfies a preset criterion; and in response to the expression satisfying the preset criterion, deeming the first fault scenario as subsumed by the second fault scenario.
This invention relates to fault diagnosis systems, specifically methods for refining and organizing fault scenarios to improve diagnostic accuracy and efficiency. The problem addressed is the redundancy and ambiguity in fault scenarios, which can lead to inefficient or incorrect diagnoses. The method involves analyzing relationships between fault scenarios to identify and resolve redundancies. The method includes determining an expression that relates a first fault scenario to a second fault scenario, where the first scenario is treated as a parameter in the expression. The expression is evaluated to determine if it meets a preset criterion, such as logical consistency or diagnostic relevance. If the criterion is satisfied, the first fault scenario is deemed subsumed by the second, meaning the second scenario encompasses the first. This process helps eliminate redundant scenarios, ensuring that only distinct and meaningful fault scenarios are retained for diagnosis. The method may also involve comparing multiple fault scenarios to identify hierarchical relationships, where one scenario is a more general or specific case of another. By refining the set of fault scenarios in this way, the system can improve diagnostic accuracy by reducing ambiguity and ensuring that only relevant scenarios are considered during fault detection and analysis. This approach is particularly useful in complex systems where numerous potential faults may exist, and distinguishing between them is critical for effective troubleshooting.
38. The method of claim 23 , wherein the matching of the set of observed symptoms includes adding a PMPFS that handles partial matches with a matching mechanism.
39. The method of claim 38 , wherein the adding of the PMPFS comprises allowing a potential fault scenario to specify a given symptom as excluding one or more values.
40. The method of claim 38 , wherein the adding of the PMPFS comprises allowing a potential fault scenario to restrict a scope of the PMPFS and its symptoms and correspondingly reduce probability associated with the PMPFS.
41. The method of claim 38 , wherein the adding of the PMPFS comprises allowing a potential fault scenario to define an aggregate symptom across multiple inputs.
This invention relates to fault detection and diagnosis in systems with multiple inputs, particularly in scenarios where a single fault can manifest as an aggregate symptom across different inputs. The problem addressed is the challenge of accurately identifying and diagnosing faults when symptoms are distributed or combined across multiple system inputs, making it difficult to isolate the root cause. The method involves adding a Probabilistic Multi-Input Fault Signature (PMPFS) to a fault detection system. The PMPFS allows a potential fault scenario to define an aggregate symptom that spans multiple inputs, rather than treating each input independently. This approach improves fault diagnosis by correlating symptoms across inputs, enabling more accurate identification of the underlying fault. The method may also include generating a fault signature matrix that maps faults to their corresponding aggregate symptoms, enhancing the system's ability to distinguish between different fault scenarios. By integrating the PMPFS, the system can better handle complex fault conditions where a single fault affects multiple inputs, reducing false positives and improving diagnostic accuracy. This is particularly useful in systems where inputs are interdependent or where faults produce indirect or compounded effects. The method may also involve updating the PMPFS dynamically as new fault data is collected, ensuring the system remains adaptive to evolving fault patterns.
42. The method of claim 38 , wherein the adding of the PMPFS comprises excluding from the PMPFS a set of back propagated symptoms.
43. The method of claim 42 , wherein adding of the PMPFS comprises at least one of the following: using a subset of back-propagated symptoms; using an aggregate of back-propagated symptoms; and using exclusion of back-propagated symptom values.
44. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for: receiving a set of observed symptoms of a physical system; matching the set of observed symptoms with a database of fault scenarios to generate a plurality of matched fault scenarios, wherein the database of fault scenarios comprises combinations of symptoms and corresponding root cause identifiers; and refining the plurality of matched fault scenarios, comprising: determining whether a first fault scenario of the plurality of matched fault scenarios is to be subsumed by a second fault scenario; and in response to a determination that the first fault scenario of the plurality of matched fault scenarios is to be subsumed by the second fault scenario: including the second fault scenario in the plurality of matched fault scenarios, deprioritizing the first fault scenario, or both including the second fault scenario in the plurality of matched fault scenarios and deprioritizing the first fault scenario; and outputting the plurality of matched fault scenarios as potential root causes.
Unknown
March 16, 2021
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